Emily Pfaff

48 papers receiving 672 citations

Emily Pfaff's Hit Papers

Coding long COVID: characterizing a new disease through an ICD-10 lens 2023 · 93 citations
930+1+2Years since publication255075

Peers

Emily Pfaff
Comparison fields: 5 of 84
  • Health Information Management 70
  • Neurology 112
  • Health Informatics 10
  • Health, Toxicology and Mutagenesis 59
  • Hematology 38
Replace Venexia Walker with:
Venexia Walker United Kingdom
Yu‐Tse Tsan Taiwan
Chengyun Liu China
Jacques Kpodonu United States
Devon J. Boyne Canada
Corran Roberts United Kingdom
Joao H. Bettencourt‐Silva United Kingdom
Giuseppe Russo Italy
Kjersti Mørkrid Norway
Emily Pfaff relative to Venexia Walker United Kingdom Venexia Walker's profile →
Citations per field
00.5×11.7×
Venexia Walker · 1×
Citations per year

Countries citing papers authored by Emily Pfaff

Since Specialization
Citations

This map shows the geographic impact of Emily Pfaff's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Emily Pfaff with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Emily Pfaff more than expected).

Fields of papers citing papers by Emily Pfaff

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Emily Pfaff. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Emily Pfaff. The network helps show where Emily Pfaff may publish in the future.

Co-authors

The 25 scholars most cited alongside Emily Pfaff, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Emily Pfaff Line = papers co-authored together Emily Pfaff links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Coding long COVID: characterizing a new disease through an ICD-10 lens
Hit paper breakdown →
202393
2 201851
3 201451
4 201944
5 201543
6 202033
7 201331
8 202331
9 202029
10 201629
11 202223
12 202420
13 202217
14 201917
15 201915
16 201913
17 202012
18 202112
19 202211
20 202310

About Emily Pfaff

Emily Pfaff is a scholar working on Neurology, Health Information Management, Artificial Intelligence, Health, Toxicology and Mutagenesis and Public Health, Environmental and Occupational Health, having authored 50 papers that have together received 682 indexed citations. Recurring topics across this work include Long-Term Effects of COVID-19 (7 papers), Machine Learning in Healthcare (4 papers), Electronic Health Records Systems (3 papers), Artificial Intelligence in Healthcare (3 papers), COVID-19 Clinical Research Studies (3 papers), Ethics in Clinical Research (3 papers), Cardiovascular Health and Risk Factors (2 papers) and Climate Change and Health Impacts (2 papers). The work is most often cited by research in Health Information Management (70 citations), Neurology (112 citations), Health Informatics (10 citations), Health, Toxicology and Mutagenesis (59 citations) and Hematology (38 citations). Emily Pfaff has collaborated with scholars based in United States, Norway and China. Frequent co-authors include Christopher G. Chute, Melissa Haendel, Karamarie Fecho, Stanley C. Ahalt, Johanna Loomba, Richard A. Moffitt, Robert L. Bradford, Elaine Hill, Julie A. McMurry and Ashok Krishnamurthy. Their work appears in journals such as Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, PLoS Medicine, Journal of Medical Internet Research and BMC Medical Research Methodology.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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